Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [3]:
#load data
df = px.data.gapminder()
df.head()
Out[3]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [12]:
# YOUR CODE HERE
df_world = px.data.gapminder()
data_year = df_world.query("year==2007")

fig = px.bar(data_year, x="continent", y="pop", animation_frame="year", animation_group="country",
           color="continent", range_y=[0,4000000000])
fig.update_xaxes(categoryorder='total descending')
fig["layout"].pop("updatemenus") # optional, drop animation buttons
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [6]:
# YOUR CODE HERE

Question 3:¶

Add text to each bar that represents the population

In [ ]:
# YOUR CODE HERE
df_world = px.data.gapminder()
data_year = df_world.query("year==2007")

fig = px.bar(data_year, x="continent", y="pop", animation_frame="year", animation_group="country",
           color="continent", range_y=[0,4000000000])
fig.update_xaxes(categoryorder='total descending')
fig["layout"].pop("updatemenus") # optional, drop animation buttons
fig.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [14]:
# YOUR CODE HERE

df = px.data.gapminder()
fig = px.bar(df_world, x="continent", y="pop", animation_frame="year", animation_group="country",
           color="continent", range_y=[0,4000000000])
fig.update_xaxes(categoryorder='total descending')
fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [16]:
# YOUR CODE HERE

df = px.data.gapminder()
fig = px.bar(df_world, x="pop", y="country", animation_frame="year", animation_group="country",
           color="country", range_x=[0,1500000000])
fig.update_yaxes(categoryorder='total descending')
fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [17]:
# YOUR CODE HERE

df = px.data.gapminder()
fig = px.bar(df_world, x="pop", y="country", animation_frame="year", animation_group="country",
           color="country", range_x=[0,1500000000], height=3000)
fig.update_yaxes(categoryorder='total ascending')
fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [18]:
# YOUR CODE HERE

df = px.data.gapminder()
fig = px.bar(df_world, x="pop", y="country", animation_frame="year", animation_group="country",
           color="country", range_x=[0,1500000000])
fig.update_yaxes(categoryorder='total ascending')
fig.update_yaxes(range=(131.5, 141.5))
fig.show()
In [ ]: